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Artificial intelligence in sales research: Identifying emergent themes and looking forward

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  • Jarotschkin, Viktor
  • Soykoth, Mostofa Wahid
  • Chaker, Nawar N.

Abstract

The rapid advancement of artificial intelligence (AI) in sales lends promising grounds for sales practice and academic research. Using a two-study, multi-method approach to examining the literature, this study provides a detailed overview of the current position of AI in sales research. We consider how AI is studied as a substantive topic and how it is used as an analytical tool to study sales phenomena. Study 1 uses bibliometric analysis to provide a “horizontal” view of the literature by uncovering the network characteristics and structure. Study 2 uses topic modeling based on Latent Dirichlet Allocation (LDA)—a machine learning (ML) approach—to offer a “vertical” view of the literature by plunging into the contents of each article to reveal five important themes that characterize the state of the literature. We also offer a future research agenda to guide more studies that integrate AI and sales.

Suggested Citation

  • Jarotschkin, Viktor & Soykoth, Mostofa Wahid & Chaker, Nawar N., 2025. "Artificial intelligence in sales research: Identifying emergent themes and looking forward," Journal of Business Research, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:jbrese:v:198:y:2025:i:c:s0148296325002061
    DOI: 10.1016/j.jbusres.2025.115383
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    1. Pappas, Alec & Fumagalli, Elena & Rouziou, Maria & Bolander, Willy, 2023. "More than Machines: The Role of the Future Retail Salesperson in Enhancing the Customer Experience," Journal of Retailing, Elsevier, vol. 99(4), pages 518-531.
    2. Mukherjee, Debmalya & Lim, Weng Marc & Kumar, Satish & Donthu, Naveen, 2022. "Guidelines for advancing theory and practice through bibliometric research," Journal of Business Research, Elsevier, vol. 148(C), pages 101-115.
    3. Zoran Latinovic & Sharmila C. Chatterjee, 2022. "Achieving the promise of AI and ML in delivering economic and relational customer value in B2B," Post-Print hal-04717609, HAL.
    4. Song, Christina Soyoung & Kim, Youn-Kyung, 2021. "Predictors of consumers’ willingness to share personal information with fashion sales robots," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    5. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    6. Wong, W.K. & Leung, S.Y.S. & Guo, Z.X. & Zeng, X.H. & Mok, P.Y., 2012. "Intelligent product cross-selling system with radio frequency identification technology for retailing," International Journal of Production Economics, Elsevier, vol. 135(1), pages 308-319.
    7. Minkyung Kim & K. Sudhir & Kosuke Uetake, 2022. "A Structural Model of a Multitasking Salesforce: Incentives, Private Information, and Job Design," Management Science, INFORMS, vol. 68(6), pages 4602-4630, June.
    8. Yael Karlinsky-Shichor & Oded Netzer, 2024. "Automating the B2B Salesperson Pricing Decisions: A Human-Machine Hybrid Approach," Marketing Science, INFORMS, vol. 43(1), pages 138-157, January.
    9. Norris, Michael & Oppenheim, Charles, 2007. "Comparing alternatives to the Web of Science for coverage of the social sciences’ literature," Journal of Informetrics, Elsevier, vol. 1(2), pages 161-169.
    10. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    11. J. D Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.
    12. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    13. Ashish Goel & Ashwin Baliga & Deva Rangarajan & Bruno Lussier, 2024. "Technology use in B2B sales: examining the extant literature and identifying future research opportunities using morphological analysis," Post-Print hal-04835837, HAL.
    14. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    15. Dennis Herhausen & Stefan F. Bernritter & Eric W. T. Ngai & Ajay Kumar & Dursun Delen, 2024. "Machine learning in marketing : Recent progress and future research directions," Post-Print hal-04339463, HAL.
    16. Fan, Zhi-Ping & Sun, Minghe, 2016. "A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendationsAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 255(1), pages 110-120.
    17. Nathaniel N. Hartmann & Heiko Wieland & Brandon Gustafson & Johannes Habel, 2024. "Research on sales and ethics: Mapping the past and charting the future," Journal of the Academy of Marketing Science, Springer, vol. 52(3), pages 653-671, May.
    18. Loureiro, Sandra Maria Correia & Guerreiro, João & Eloy, Sara & Langaro, Daniela & Panchapakesan, Padma, 2019. "Understanding the use of Virtual Reality in Marketing: A text mining-based review," Journal of Business Research, Elsevier, vol. 100(C), pages 514-530.
    19. Chang, Woojung, 2022. "The effectiveness of AI salesperson vs. human salesperson across the buyer-seller relationship stages," Journal of Business Research, Elsevier, vol. 148(C), pages 241-251.
    20. Adam, Martin & Röthke, Konstantin & Benlian, Alexander, 2023. "Human Versus Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 134830, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    21. Philippe Mongeon & Adèle Paul-Hus, 2016. "The journal coverage of Web of Science and Scopus: a comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 213-228, January.
    22. John Hulland, 2024. "Bibliometric reviews—some guidelines," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 935-938, July.
    23. Chen, Serena H. & Jakeman, Anthony J. & Norton, John P., 2008. "Artificial Intelligence techniques: An introduction to their use for modelling environmental systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 379-400.
    24. Mekhail Mustak & Joni Salminen & Loïc Plé & Jochen Wirtz, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Post-Print hal-03269994, HAL.
    25. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
    26. Tomaz Bartol & Gordana Budimir & Doris Dekleva-Smrekar & Miro Pusnik & Primoz Juznic, 2014. "Assessment of research fields in Scopus and Web of Science in the view of national research evaluation in Slovenia," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1491-1504, February.
    27. Andris A. Zoltners & Prabhakant Sinha & Philip S. C. Chong, 1979. "An Optimal Algorithm for Sales Representative Time Management," Management Science, INFORMS, vol. 25(12), pages 1197-1207, December.
    28. Grewal, Dhruv & Kroschke, Mirja & Mende, Martin & Roggeveen, Anne L. & Scott, Maura L., 2020. "Frontline Cyborgs at Your Service: How Human Enhancement Technologies Affect Customer Experiences in Retail, Sales, and Service Settings," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 9-25.
    29. Latinovic, Zoran & Chatterjee, Sharmila C., 2022. "Achieving the promise of AI and ML in delivering economic and relational customer value in B2B," Journal of Business Research, Elsevier, vol. 144(C), pages 966-974.
    30. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    31. Colleen Mcclure & Rhett Epler & Laurianne Schmitt & Deva Rangarajan, 2024. "AI in sales: Laying the foundations for future research," Post-Print hal-04835906, HAL.
    32. Ian Yi Han Ang & Ruth F. Lewis & Jason C. H. Yap, 2025. "Getting Healthier, Aging Well in Singapore," Springer Books, in: Volker Amelung & Viktoria Stein & Esther Suter & Nicholas Goodwin & Ran Balicer & Anna-Sophia Beese (ed.), Handbook of Integrated Care, edition 0, chapter 80, pages 1549-1569, Springer.
    33. Paschen, Jeannette & Wilson, Matthew & Ferreira, João J., 2020. "Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel," Business Horizons, Elsevier, vol. 63(3), pages 403-414.
    34. João Guerreiro & Paulo Rita & Duarte Trigueiros, 2016. "A Text Mining-Based Review of Cause-Related Marketing Literature," Journal of Business Ethics, Springer, vol. 139(1), pages 111-128, November.
    35. Michael Rodriguez & Robert Peterson, 2024. "Artificial intelligence in business-to-business (B2B) sales process: a conceptual framework," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 778-789, December.
    36. Martin Adam & Konstantin Roethke & Alexander Benlian, 2023. "Human vs. Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages," Information Systems Research, INFORMS, vol. 34(3), pages 1148-1168, September.
    37. Jaeho Choi & Anoop Menon & Haris Tabakovic, 2021. "Using machine learning to revisit the diversification–performance relationship," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1632-1661, September.
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